In [ ]:
!git clone https://github.com/ultralytics/yolov5 # clone repo
!pip install -qr yolov5/requirements.txt # install dependencies (ignore errors)
%cd yolov5
import torch
from IPython.display import Image, clear_output # to display images
from utils.google_utils import gdrive_download # to download models/datasets
clear_output()
print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))
Setup complete. Using torch 1.6.0+cu101 _CudaDeviceProperties(name='Tesla K80', major=3, minor=7, total_memory=11441MB, multi_processor_count=13)
In [ ]:
# Export code snippet and paste here
%cd /content
!curl -L "YOUR LINK !!!!!!!!" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip
/content
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 896 100 896 0 0 787 0 0:00:01 0:00:01 --:--:-- 788
100 120M 100 120M 0 0 26.9M 0 0:00:04 0:00:04 --:--:-- 38.5M
Archive: roboflow.zip
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In [ ]:
# this is the YAML file Roboflow wrote for us that we're loading into this notebook with our data
%cat data.yaml
train: ../train/images val: ../valid/images nc: 3 names: ['mask_weared_incorrect', 'with_mask', 'without_mask']
Define Model Configuration and Architecture¶
We will write a yaml script that defines the parameters for our model like the number of classes, anchors, and each layer.
You do not need to edit these cells, but you may.
In [ ]:
# define number of classes based on YAML
import yaml
with open("data.yaml", 'r') as stream:
num_classes = str(yaml.safe_load(stream)['nc'])
In [ ]:
#this is the model configuration we will use for our tutorial
%cat /content/yolov5/models/yolov5s.yaml
# parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, BottleneckCSP, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, BottleneckCSP, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, BottleneckCSP, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, BottleneckCSP, [1024, False]], # 9 ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]
In [ ]:
#customize iPython writefile so we can write variables
from IPython.core.magic import register_line_cell_magic
@register_line_cell_magic
def writetemplate(line, cell):
with open(line, 'w') as f:
f.write(cell.format(**globals()))
In [ ]:
%%writetemplate /content/yolov5/models/custom_yolov5s.yaml
# parameters
nc: {num_classes} # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9
]
# YOLOv5 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
Train Custom YOLOv5 Detector¶
Next, we'll fire off training!¶
Here, we are able to pass a number of arguments:
- img: define input image size
- batch: determine batch size
- epochs: define the number of training epochs. (Note: often, 3000+ are common here!)
- data: set the path to our yaml file
- cfg: specify our model configuration
- weights: specify a custom path to weights. (Note: you can download weights from the Ultralytics Google Drive folder)
- name: result names
- nosave: only save the final checkpoint
- cache: cache images for faster training
In [ ]:
# train yolov5s on custom data for 100 epochs
# time its performance
%%time
%cd /content/yolov5/
!python train.py --img 416 --batch 16 --epochs 200 --data '../data.yaml' --cfg ./models/custom_yolov5s.yaml --weights '' --name yolov5s_results --cache
/content/yolov5
Using CUDA device0 _CudaDeviceProperties(name='Tesla K80', total_memory=11441MB)
Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='./models/custom_yolov5s.yaml', data='../data.yaml', device='', epochs=200, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[416, 416], local_rank=-1, logdir='runs/', multi_scale=False, name='yolov5s_results', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='', workers=8, world_size=1)
Start Tensorboard with "tensorboard --logdir runs/", view at http://localhost:6006/
2020-09-05 12:56:38.604581: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mixup': 0.0}
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
24 [17, 20, 23] 1 21576 models.yolo.Detect [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 191 layers, 7.26049e+06 parameters, 7.26049e+06 gradients
Optimizer groups: 62 .bias, 70 conv.weight, 59 other
Scanning images: 100% 1782/1782 [00:00<00:00, 4747.92it/s]
Scanning labels ../train/labels.cache (1782 found, 0 missing, 0 empty, 0 duplicate, for 1782 images): 1782it [00:00, 11294.88it/s]
Caching images (0.9GB): 100% 1782/1782 [00:06<00:00, 292.21it/s]
Scanning images: 100% 170/170 [00:00<00:00, 3221.94it/s]
Scanning labels ../valid/labels.cache (170 found, 0 missing, 0 empty, 0 duplicate, for 170 images): 170it [00:00, 4766.19it/s]
Caching images (0.1GB): 100% 170/170 [00:00<00:00, 219.56it/s]
/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
import pandas.util.testing as tm
Analyzing anchors... anchors/target = 5.18, Best Possible Recall (BPR) = 0.9979
Image sizes 416 train, 416 test
Using 2 dataloader workers
Logging results to runs/exp0_yolov5s_results
Starting training for 200 epochs...
Epoch gpu_mem GIoU obj cls total targets img_size
0/199 0.851G 0.1016 0.1061 0.02892 0.2366 19 416: 100% 112/112 [00:56<00:00, 1.97it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:08<00:00, 1.36it/s]
all 170 778 0 0 0.000495 9.2e-05
Epoch gpu_mem GIoU obj cls total targets img_size
1/199 1.78G 0.09766 0.1104 0.02147 0.2295 28 416: 100% 112/112 [00:49<00:00, 2.25it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:04<00:00, 2.26it/s]
all 170 778 0 0 0.000465 7.97e-05
Epoch gpu_mem GIoU obj cls total targets img_size
2/199 1.78G 0.09391 0.1079 0.01924 0.221 34 416: 100% 112/112 [00:48<00:00, 2.31it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:04<00:00, 2.60it/s]
all 170 778 0.0191 0.182 0.0441 0.00858
Epoch gpu_mem GIoU obj cls total targets img_size
3/199 1.78G 0.08003 0.09705 0.01734 0.1944 49 416: 100% 112/112 [00:48<00:00, 2.31it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:04<00:00, 2.66it/s]
all 170 778 0.0177 0.267 0.104 0.0292
Epoch gpu_mem GIoU obj cls total targets img_size
4/199 1.78G 0.07252 0.09229 0.01665 0.1815 21 416: 100% 112/112 [00:48<00:00, 2.33it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:03<00:00, 3.05it/s]
all 170 778 0.0615 0.259 0.185 0.0675
Epoch gpu_mem GIoU obj cls total targets img_size
5/199 1.78G 0.06773 0.08678 0.01566 0.1702 25 416: 100% 112/112 [00:47<00:00, 2.34it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:04<00:00, 2.64it/s]
all 170 778 0.0835 0.259 0.142 0.0491
Epoch gpu_mem GIoU obj cls total targets img_size
6/199 1.78G 0.06348 0.08315 0.01403 0.1607 59 416: 100% 112/112 [00:47<00:00, 2.34it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:03<00:00, 2.91it/s]
all 170 778 0.0878 0.385 0.271 0.105
Epoch gpu_mem GIoU obj cls total targets img_size
7/199 1.78G 0.06233 0.07929 0.0123 0.1539 27 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:03<00:00, 3.17it/s]
all 170 778 0.134 0.417 0.213 0.0737
Epoch gpu_mem GIoU obj cls total targets img_size
8/199 1.78G 0.06112 0.0762 0.01213 0.1495 25 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:03<00:00, 3.61it/s]
all 170 778 0.194 0.427 0.315 0.127
Epoch gpu_mem GIoU obj cls total targets img_size
9/199 1.78G 0.05562 0.07393 0.01129 0.1408 12 416: 100% 112/112 [00:47<00:00, 2.35it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 3.89it/s]
all 170 778 0.212 0.464 0.393 0.164
Epoch gpu_mem GIoU obj cls total targets img_size
10/199 1.78G 0.05245 0.07192 0.01035 0.1347 37 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 3.72it/s]
all 170 778 0.238 0.443 0.416 0.192
Epoch gpu_mem GIoU obj cls total targets img_size
11/199 1.78G 0.04924 0.07222 0.01034 0.1318 54 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.15it/s]
all 170 778 0.264 0.477 0.448 0.207
Epoch gpu_mem GIoU obj cls total targets img_size
12/199 1.78G 0.04872 0.0719 0.009624 0.1302 33 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 3.83it/s]
all 170 778 0.269 0.521 0.49 0.242
Epoch gpu_mem GIoU obj cls total targets img_size
13/199 1.78G 0.04647 0.06841 0.009786 0.1247 24 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.24it/s]
all 170 778 0.285 0.524 0.499 0.247
Epoch gpu_mem GIoU obj cls total targets img_size
14/199 1.78G 0.04587 0.06814 0.009403 0.1234 21 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.07it/s]
all 170 778 0.243 0.543 0.499 0.243
Epoch gpu_mem GIoU obj cls total targets img_size
15/199 1.78G 0.04465 0.06655 0.009046 0.1202 24 416: 100% 112/112 [00:47<00:00, 2.38it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.30it/s]
all 170 778 0.316 0.53 0.504 0.255
Epoch gpu_mem GIoU obj cls total targets img_size
16/199 1.78G 0.0436 0.06391 0.008452 0.116 50 416: 100% 112/112 [00:47<00:00, 2.35it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.17it/s]
all 170 778 0.29 0.54 0.513 0.261
Epoch gpu_mem GIoU obj cls total targets img_size
17/199 1.78G 0.04262 0.06484 0.009112 0.1166 21 416: 100% 112/112 [00:47<00:00, 2.37it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.35it/s]
all 170 778 0.256 0.555 0.529 0.263
Epoch gpu_mem GIoU obj cls total targets img_size
18/199 1.78G 0.04142 0.06344 0.008661 0.1135 41 416: 100% 112/112 [00:47<00:00, 2.38it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.10it/s]
all 170 778 0.256 0.563 0.53 0.283
Epoch gpu_mem GIoU obj cls total targets img_size
19/199 1.78G 0.04104 0.06225 0.008169 0.1115 31 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.39it/s]
all 170 778 0.332 0.537 0.528 0.283
Epoch gpu_mem GIoU obj cls total targets img_size
20/199 1.78G 0.04076 0.06161 0.008172 0.1105 24 416: 100% 112/112 [00:47<00:00, 2.37it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.02it/s]
all 170 778 0.32 0.521 0.52 0.271
Epoch gpu_mem GIoU obj cls total targets img_size
21/199 1.78G 0.03991 0.06284 0.007879 0.1106 66 416: 100% 112/112 [00:47<00:00, 2.37it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.29it/s]
all 170 778 0.334 0.587 0.533 0.299
Epoch gpu_mem GIoU obj cls total targets img_size
22/199 1.78G 0.04043 0.06182 0.007992 0.1102 44 416: 100% 112/112 [00:48<00:00, 2.33it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.37it/s]
all 170 778 0.335 0.555 0.545 0.303
Epoch gpu_mem GIoU obj cls total targets img_size
23/199 1.78G 0.03912 0.06082 0.007495 0.1074 14 416: 100% 112/112 [00:47<00:00, 2.34it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.37it/s]
all 170 778 0.322 0.63 0.55 0.308
Epoch gpu_mem GIoU obj cls total targets img_size
24/199 1.78G 0.03778 0.06013 0.007294 0.1052 29 416: 100% 112/112 [00:47<00:00, 2.36it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 11/11 [00:02<00:00, 4.49it/s]
all 170 778 0.436 0.55 0.545 0.307
Epoch gpu_mem GIoU obj cls total targets img_size
25/199 1.78G 0.0374 0.05856 0.007158 0.1031 95 416: 97% 109/112 [00:46<00:01, 2.30it/s]
Evaluate Custom YOLOv5 Detector Performance¶
Training losses and performance metrics are saved to Tensorboard and also to a logfile defined above with the --name flag when we train. In our case, we named this yolov5s_results. (If given no name, it defaults to results.txt.) The results file is plotted as a png after training completes.
Note from Glenn: Partially completed results.txt files can be plotted with from utils.utils import plot_results; plot_results().
In [ ]:
# Start tensorboard
# Launch after you have started training
# logs save in the folder "runs"
%load_ext tensorboard
%tensorboard --logdir runs
In [ ]:
# we can also output some older school graphs if the tensor board isn't working for whatever reason...
from utils.general import plot_results;
# plot_results() # plot results.txt files as results.png
Image(filename='./runs/exp0_yolov5s_results/results.png', width=1000) # view results.png
Out[ ]:
In [ ]:
Image(filename='./runs/exp0_yolov5s_results/labels.png', width=1000) # view labels.png
Out[ ]:
In [ ]:
# first, display our ground truth data
print("GROUND TRUTH TRAINING DATA:")
Image(filename='./runs/exp0_yolov5s_results/test_batch0_gt.jpg', width=900)
GROUND TRUTH TRAINING DATA:
Out[ ]:
In [ ]:
# print out an augmented training example
print("GROUND TRUTH AUGMENTED TRAINING DATA:")
Image(filename='./runs/exp0_yolov5s_results/train_batch0.jpg', width=900)
GROUND TRUTH AUGMENTED TRAINING DATA:
Out[ ]:
Run Inference With Trained Weights¶
Run inference with a pretrained checkpoint on contents of test/images folder downloaded from Roboflow.
In [ ]:
# trained weights are saved by default in our weights folder
%ls runs/
exp0_yolov5s_results/
In [ ]:
%ls runs/exp0_yolov5s_results/weights
best.pt last.pt
In [ ]:
# when we ran this, we saw .007 second inference time. That is 140 FPS on a TESLA P100!
# use the best weights!
%cd /content/yolov5/
!python detect.py --weights runs/exp0_yolov5s_results/weights/best.pt --img 416 --conf 0.1 --source ../test/images
/content/yolov5 Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.1, device='', img_size=416, iou_thres=0.5, output='inference/output', save_txt=False, source='../test/images', update=False, view_img=False, weights=['runs/exp0_yolov5s_results/weights/best.pt']) Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB) Fusing layers... Model Summary: 140 layers, 7.25191e+06 parameters, 6.61683e+06 gradients image 1/84 /content/test/images/maksssksksss101_png.rf.7d86eac730da32386a7e7d0cc59c10a6.jpg: 416x416 1 with_masks, Done. (0.015s) image 2/84 /content/test/images/maksssksksss140_png.rf.6b65df8f16bf8f488c6897fb0176c0fa.jpg: 416x416 2 with_masks, Done. (0.014s) image 3/84 /content/test/images/maksssksksss145_png.rf.dd5c4a145432ff5f29bbad20959ef5a1.jpg: 416x416 2 mask_weared_incorrects, 4 with_masks, 2 without_masks, Done. (0.014s) image 4/84 /content/test/images/maksssksksss150_png.rf.4e265e4157f0b6eb4fee5d684db635cb.jpg: 416x416 1 with_masks, Done. (0.019s) image 5/84 /content/test/images/maksssksksss154_png.rf.faf7c9a4909c707059f3f9c0752bc3d2.jpg: 416x416 1 without_masks, Done. (0.015s) image 6/84 /content/test/images/maksssksksss159_png.rf.273ae0ac2a58d2bffb540628db089ff7.jpg: 416x416 11 with_masks, 1 without_masks, Done. (0.015s) image 7/84 /content/test/images/maksssksksss174_png.rf.d59800c9e92239e4c6096f14d29a8568.jpg: 416x416 4 with_masks, Done. (0.015s) image 8/84 /content/test/images/maksssksksss176_png.rf.5eea489c2094696730d3bca2c10a19c0.jpg: 416x416 1 mask_weared_incorrects, 13 with_masks, 7 without_masks, Done. (0.015s) image 9/84 /content/test/images/maksssksksss178_png.rf.198f9e23d848598c6ae8c40d185fe814.jpg: 416x416 2 mask_weared_incorrects, 1 with_masks, 4 without_masks, Done. (0.015s) image 10/84 /content/test/images/maksssksksss200_png.rf.ac137a95e2cc262db4ea957f99bbd6fc.jpg: 416x416 1 mask_weared_incorrects, 2 with_masks, 1 without_masks, Done. (0.015s) image 11/84 /content/test/images/maksssksksss216_png.rf.af5171bfe621c28e28a1d39123fd0643.jpg: 416x416 1 mask_weared_incorrects, 5 with_masks, 5 without_masks, Done. (0.018s) image 12/84 /content/test/images/maksssksksss221_png.rf.ee9b55a1ab6a1ec2fb894884159fff30.jpg: 416x416 9 with_masks, 2 without_masks, Done. (0.019s) image 13/84 /content/test/images/maksssksksss238_png.rf.d359fe00331f14508131136f615af20d.jpg: 416x416 11 with_masks, 1 without_masks, Done. (0.015s) image 14/84 /content/test/images/maksssksksss250_png.rf.19485ed016fd11510885f66c11e3fa64.jpg: 416x416 3 mask_weared_incorrects, 5 with_masks, 4 without_masks, Done. (0.015s) image 15/84 /content/test/images/maksssksksss261_png.rf.a99b49da38aff09147bd7221a1a964ff.jpg: 416x416 3 mask_weared_incorrects, 3 with_masks, 5 without_masks, Done. (0.014s) image 16/84 /content/test/images/maksssksksss273_png.rf.be4e2020f891b087f41004e736482795.jpg: 416x416 2 with_masks, Done. (0.015s) image 17/84 /content/test/images/maksssksksss28_png.rf.da81e59bfaea6afaca76cd33875255a0.jpg: 416x416 1 with_masks, 1 without_masks, Done. (0.022s) image 18/84 /content/test/images/maksssksksss291_png.rf.476e11f58baff0ca600db731c5eb935c.jpg: 416x416 1 with_masks, Done. (0.015s) image 19/84 /content/test/images/maksssksksss298_png.rf.81f929e7bf9f373cf13c84ab0533b71f.jpg: 416x416 2 with_masks, Done. (0.015s) image 20/84 /content/test/images/maksssksksss299_png.rf.160eb486a02b49f03562749eb3c5575a.jpg: 416x416 1 without_masks, Done. (0.021s) image 21/84 /content/test/images/maksssksksss308_png.rf.3b2414cf346e30c5b77dd33d0b4f09dc.jpg: 416x416 3 with_masks, Done. (0.017s) image 22/84 /content/test/images/maksssksksss320_png.rf.633b8a436251d4380098e8c505c326ff.jpg: 416x416 3 with_masks, 2 without_masks, Done. (0.015s) image 23/84 /content/test/images/maksssksksss343_png.rf.66932a3831462fb4d5c75d01da9a42c1.jpg: 416x416 1 without_masks, Done. (0.014s) image 24/84 /content/test/images/maksssksksss354_png.rf.370796e8c5e9cb5bae9e3c91e1443f42.jpg: 416x416 35 with_masks, 11 without_masks, Done. (0.016s) image 25/84 /content/test/images/maksssksksss388_png.rf.1179008d0237418ce51d8ea870a96d80.jpg: 416x416 3 mask_weared_incorrects, 10 with_masks, 11 without_masks, Done. (0.015s) image 26/84 /content/test/images/maksssksksss400_png.rf.b8863e17c78e9fa53f45a3d1915a5e2f.jpg: 416x416 1 mask_weared_incorrects, 4 with_masks, 2 without_masks, Done. (0.015s) image 27/84 /content/test/images/maksssksksss407_png.rf.25650e7b07ac6d3b9bd3b2e9c7ce5c8b.jpg: 416x416 2 with_masks, Done. (0.014s) image 28/84 /content/test/images/maksssksksss423_png.rf.19f044f2ee1392acf0e5f05cb726680c.jpg: 416x416 12 with_masks, 1 without_masks, Done. (0.014s) image 29/84 /content/test/images/maksssksksss425_png.rf.d271a149e5307d31153441aad605c9f4.jpg: 416x416 1 with_masks, Done. (0.015s) image 30/84 /content/test/images/maksssksksss434_png.rf.76202edeff208f01b486247163c735c3.jpg: 416x416 1 with_masks, Done. (0.014s) image 31/84 /content/test/images/maksssksksss443_png.rf.03cddc0cd196dee7465f0a6471bb4ddf.jpg: 416x416 1 mask_weared_incorrects, 1 without_masks, Done. (0.014s) image 32/84 /content/test/images/maksssksksss444_png.rf.834839f5a3e30417dd77d8e9f4c3a27d.jpg: 416x416 5 with_masks, 1 without_masks, Done. (0.014s) image 33/84 /content/test/images/maksssksksss459_png.rf.ec52a872038ba7c8412849ecfd8031cd.jpg: 416x416 1 mask_weared_incorrects, 1 with_masks, 1 without_masks, Done. (0.015s) image 34/84 /content/test/images/maksssksksss477_png.rf.a1e20366ec8b73491c8736eda6c6fdd2.jpg: 416x416 1 mask_weared_incorrects, 10 with_masks, 9 without_masks, Done. (0.014s) image 35/84 /content/test/images/maksssksksss47_png.rf.7b4a94e3eb37d736d502e82af58c72bf.jpg: 416x416 1 with_masks, Done. (0.014s) image 36/84 /content/test/images/maksssksksss487_png.rf.f97b694cbc923befb14f613de73951a7.jpg: 416x416 1 mask_weared_incorrects, 4 with_masks, 1 without_masks, Done. (0.016s) image 37/84 /content/test/images/maksssksksss488_png.rf.1e755f934a42740800548f962b9a681c.jpg: 416x416 4 with_masks, 1 without_masks, Done. (0.014s) image 38/84 /content/test/images/maksssksksss489_png.rf.139302f716d8e7b0154ede8374fd8dd6.jpg: 416x416 2 mask_weared_incorrects, 5 with_masks, 9 without_masks, Done. (0.015s) image 39/84 /content/test/images/maksssksksss491_png.rf.75813d08178ecd28e362c57f9ceb0282.jpg: 416x416 1 with_masks, Done. (0.017s) image 40/84 /content/test/images/maksssksksss499_png.rf.1b78c5eed5526d0bb20f8b89c0f1eaa0.jpg: 416x416 2 with_masks, Done. (0.014s) image 41/84 /content/test/images/maksssksksss49_png.rf.4de8894c31a6ec74256dc150c2298882.jpg: 416x416 1 mask_weared_incorrects, 8 with_masks, 2 without_masks, Done. (0.015s) image 42/84 /content/test/images/maksssksksss500_png.rf.f9b8638873fe4ebcdc48a6729cdf9dd9.jpg: 416x416 1 with_masks, Done. (0.014s) image 43/84 /content/test/images/maksssksksss508_png.rf.815e5f68890080bba6bad88f88ec440d.jpg: 416x416 5 with_masks, Done. (0.014s) image 44/84 /content/test/images/maksssksksss50_png.rf.d833a4902b2bb295abc2f248f4598ea6.jpg: 416x416 1 with_masks, Done. (0.021s) image 45/84 /content/test/images/maksssksksss525_png.rf.be01ac216861c578c7c4d7da7761b34f.jpg: 416x416 2 with_masks, 2 without_masks, Done. (0.014s) image 46/84 /content/test/images/maksssksksss526_png.rf.d817c0b23db9a5416403c6ed0ed4ed7d.jpg: 416x416 2 mask_weared_incorrects, 2 with_masks, 2 without_masks, Done. (0.016s) image 47/84 /content/test/images/maksssksksss544_png.rf.e715745c24af31b94e1dd50434919c89.jpg: 416x416 1 without_masks, Done. (0.014s) image 48/84 /content/test/images/maksssksksss546_png.rf.4b265632a568ba50ff347d83d2896adb.jpg: 416x416 1 with_masks, Done. (0.014s) image 49/84 /content/test/images/maksssksksss554_png.rf.f7f9a2cccee1da7aa2eadd5255872bd2.jpg: 416x416 5 with_masks, 1 without_masks, Done. (0.018s) image 50/84 /content/test/images/maksssksksss555_png.rf.c8229965b7565be158d6f1b628b7c536.jpg: 416x416 4 with_masks, Done. (0.015s) image 51/84 /content/test/images/maksssksksss564_png.rf.84cd0b52ead07ec557f8ac14ab9c96d4.jpg: 416x416 1 mask_weared_incorrects, 18 with_masks, 6 without_masks, Done. (0.018s) image 52/84 /content/test/images/maksssksksss585_png.rf.c59bd863b370440ef6956c9c171406d6.jpg: 416x416 6 with_masks, 2 without_masks, Done. (0.015s) image 53/84 /content/test/images/maksssksksss598_png.rf.3d6ee7d3b3f753d580ce5b362375abac.jpg: 416x416 1 with_masks, Done. (0.018s) image 54/84 /content/test/images/maksssksksss5_png.rf.4f792a4a40d91f250d2d6b1c56a0a9d9.jpg: 416x416 2 with_masks, 2 without_masks, Done. (0.017s) image 55/84 /content/test/images/maksssksksss608_png.rf.fe0cc71f7e5895ef94b43da6c823edb4.jpg: 416x416 1 with_masks, Done. (0.016s) image 56/84 /content/test/images/maksssksksss626_png.rf.7a7ac5dd4219a173655cea87ce7ad137.jpg: 416x416 1 with_masks, Done. (0.019s) image 57/84 /content/test/images/maksssksksss629_png.rf.160a0aeb9f60abddd16ab9b2d19c2881.jpg: 416x416 1 with_masks, Done. (0.018s) image 58/84 /content/test/images/maksssksksss631_png.rf.728cf452781d1a045b8631af813444a3.jpg: 416x416 13 with_masks, Done. (0.015s) image 59/84 /content/test/images/maksssksksss643_png.rf.aae5e1e36cd629d821fade6750b80fae.jpg: 416x416 1 with_masks, Done. (0.016s) image 60/84 /content/test/images/maksssksksss647_png.rf.47cefe383be06710d0100434de3cbccf.jpg: 416x416 3 mask_weared_incorrects, 12 with_masks, 6 without_masks, Done. (0.016s) image 61/84 /content/test/images/maksssksksss649_png.rf.45a8097d1a7b6d6e5586f0532da5544e.jpg: 416x416 1 with_masks, Done. (0.015s) image 62/84 /content/test/images/maksssksksss653_png.rf.50c0d4919897ddcba27cea818dfb8a10.jpg: 416x416 2 with_masks, Done. (0.015s) image 63/84 /content/test/images/maksssksksss65_png.rf.d73c0c23ea09b0c4b3887d65dfb59897.jpg: 416x416 3 with_masks, 1 without_masks, Done. (0.015s) image 64/84 /content/test/images/maksssksksss660_png.rf.375b65807d15dbfe20c7d898fc755e09.jpg: 416x416 1 without_masks, Done. (0.015s) image 65/84 /content/test/images/maksssksksss702_png.rf.545b31b7d353f08aa9db4d36edd9403c.jpg: 416x416 13 with_masks, 2 without_masks, Done. (0.015s) image 66/84 /content/test/images/maksssksksss713_png.rf.7344954f65a6f68f7052d7c353cb28e5.jpg: 416x416 1 with_masks, Done. (0.015s) image 67/84 /content/test/images/maksssksksss729_png.rf.d63b310e0e65f3076574c01c36e2a343.jpg: 416x416 1 without_masks, Done. (0.015s) image 68/84 /content/test/images/maksssksksss72_png.rf.d20dcf59f9c699d68d0e8ac69a48782a.jpg: 416x416 1 without_masks, Done. (0.014s) image 69/84 /content/test/images/maksssksksss730_png.rf.d739371e3d048688ebcb812a035d4d62.jpg: 416x416 3 with_masks, Done. (0.017s) image 70/84 /content/test/images/maksssksksss73_png.rf.4ebc156ea21e4b1b7696385606562c12.jpg: 416x416 4 with_masks, Done. (0.015s) image 71/84 /content/test/images/maksssksksss740_png.rf.b3ded6104ba5ada833e9a10ef01ece03.jpg: 416x416 3 with_masks, Done. (0.015s) image 72/84 /content/test/images/maksssksksss743_png.rf.06fd789cce923624b89cd14088f3fc5b.jpg: 416x416 1 with_masks, Done. (0.018s) image 73/84 /content/test/images/maksssksksss766_png.rf.cc27626054d7ce2c12f896fcb82f81cc.jpg: 416x416 1 with_masks, Done. (0.015s) image 74/84 /content/test/images/maksssksksss767_png.rf.218c72be4da34de5f5880cf155a35f2b.jpg: 416x416 2 with_masks, 1 without_masks, Done. (0.014s) image 75/84 /content/test/images/maksssksksss773_png.rf.cbc874f8726392af5f4edbe37f6a5d71.jpg: 416x416 4 with_masks, 5 without_masks, Done. (0.014s) image 76/84 /content/test/images/maksssksksss809_png.rf.4971d03bcebf3be6949cf8089e9ffab4.jpg: 416x416 1 mask_weared_incorrects, 2 with_masks, 1 without_masks, Done. (0.015s) image 77/84 /content/test/images/maksssksksss816_png.rf.ebc78df0c18af0d7a2c21a25da7cd881.jpg: 416x416 5 with_masks, Done. (0.014s) image 78/84 /content/test/images/maksssksksss817_png.rf.bec7bfcaf19f154c0b7a238406339b55.jpg: 416x416 1 with_masks, 1 without_masks, Done. (0.015s) image 79/84 /content/test/images/maksssksksss820_png.rf.9b3f5de520d5343c9e82a9de8945516e.jpg: 416x416 1 with_masks, Done. (0.015s) image 80/84 /content/test/images/maksssksksss828_png.rf.7799555c85bf245efb18ed399cbf4df8.jpg: 416x416 1 with_masks, 2 without_masks, Done. (0.015s) image 81/84 /content/test/images/maksssksksss835_png.rf.d53ae3c2a409ba40c47604062a2c38b0.jpg: 416x416 2 with_masks, Done. (0.018s) image 82/84 /content/test/images/maksssksksss836_png.rf.175865eae91a1e51707b79c823ae1835.jpg: 416x416 7 with_masks, Done. (0.015s) image 83/84 /content/test/images/maksssksksss848_png.rf.398f611eb8890e409d9c0d4959b0c9e2.jpg: 416x416 3 with_masks, Done. (0.014s) image 84/84 /content/test/images/maksssksksss99_png.rf.ead016e98fbca34cc0f35c1da2870984.jpg: 416x416 4 with_masks, Done. (0.014s) Results saved to inference/output Done. (2.342s)
In [ ]:
#display inference on ALL test images
#this looks much better with longer training above
import glob
from IPython.display import Image, display
for imageName in glob.glob('/content/yolov5/inference/output/*.jpg'): #assuming JPG
display(Image(filename=imageName))
print("\n")
Export Trained Weights for Future Inference¶
Now that you have trained your custom detector, you can export the trained weights you have made here for inference on your device elsewhere
In [ ]:
from google.colab import drive
drive.mount('/content/gdrive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code Enter your authorization code: ·········· Mounted at /content/gdrive
In [ ]:
# %cp /content/yolov5/weights/last_yolov5s_results.pt /content/gdrive/My\ Drive
%cp /content/yolov5/runs/exp0_yolov5s_results/weights/best.pt /content/gdrive/My\ Drive